Hostname: page-component-54dcc4c588-tfzs5 Total loading time: 0 Render date: 2025-09-12T02:17:33.964Z Has data issue: false hasContentIssue false
  • English
  • Français

Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar

Published online by Cambridge University Press:  25 March 2014

Yuan He Open the ORCID record for Yuan He [Opens in a new window] ,
Pascal Aubry ,
Francois Le Chevalier Open the ORCID record for Francois Le Chevalier [Opens in a new window]  and
Alexander Yarovoy
Yuan He*
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
Pascal Aubry
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
Francois Le Chevalier
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
Alexander Yarovoy
Affiliation:
Microwave sensing, signals and systems (MS3), Delft University of Technology, Delft, The Netherlands. Phone: +31 15 2788378
*
Corresponding author: Y. He Email: eric.yuanhe@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

A new approach is proposed to extract the slow-time feature of human motion in high-resolution radars. The approach is based on the self-similarity matrix (SSM) of the radar signals. The Mutual Information is used as a measure of similarity. The SSMs of different radar signals (high-resolution range profile, micro-Doppler, and range-Doppler video sequence) are compared, and the angel-invariant property of the SSMs is demonstrated. The SSM for different activities (i.e. walking and running) is extracted from range-Doppler video sequence and analyzed. Finally, simulation result is validated by experimental data.

Keywords

Self-similarity matrixSlow-time featureHuman target analysisHigh-resolution radar

Information

Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 6 , Issue 3-4: European Microwave Week 2013 , June 2014 , pp. 423 - 434
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

[1]Ram, S.S.; Christianson, C.; Youngwook, K.; Ling, H.: Simulation and analysis of human Micro-Dopplers in Through-Wall environments. IEEE Transact. Geosci. Remote Sens., 48 (2010), 2015–2023.CrossRefGoogle Scholar
[2]Vignaud, L.; Ghaleb, A.; Le Kernec, J.; Nicolas, J.M.: Radar high resolution range and micro-Doppler analysis of human motions, in International Radar Conf., France, 2009.Google Scholar
[3]Fogle, O.R.; Rigling, B.D.: Micro-range/micro-Doppler feature extraction and association, in IEEE Radar Conf., USA, 2011.CrossRefGoogle Scholar
[4]Chen, V.C.: Analysis of radar micro-Doppler with time-frequency transform, in Tenth IEEE Workshop on Statistical Signal and Array Processing, USA, 2000.Google Scholar
[5]Chen, V.C.: The Micro-Doppler Effect in Radar, Artech House, Norwood, MA, USA, 2011.Google Scholar
[6]Cutler, R.; Davis, L.: View-based detection and analysis of periodic motion, in Fourteenth Int. Conf. Pattern Recognition, Australia, 1998.Google Scholar
[7]BenAbdelkader, C.; Cutler, R.; Davis, L.: Gait recognition using image self-similarity. EURASIP J. Appl. Signal Process., 4 (2004) 572–585.Google Scholar
[8]Junejo, I.N.; Dexter, E.; Laptev, I.; Perez, P.: View-independent action recognition from temporal self-similarities. IEEE Transact. Pattern Anal. Mach. Intell., 33 (2011), 172–185.Google Scholar
[9]He, Y.; Le Chevalier, F.; Yarovoy, A.G.: Association of range-doppler video sequences in multistatic UWB radar for human tracking, in European Radar Conf., Netherlands, 2012.Google Scholar
[10]Van Dorp, P.; Groen, F.C.A.: Human walking estimation with radar. IEE Proc. Radar, Sonar Navig., 150 (2003), 356–365.Google Scholar
[11]Motion capture database. http://mocap.cs.cmu.edu/.Google Scholar
[12]Eckmann, J.P.; Kamphorst, S.O.; Ruelle, D.: Recurrence plots of dynamical systems. Europhys. Lett., 4 (1987), 973–977.Google Scholar
[13]Cutler, R.; Davis, L.: Robust real-time periodic motion detection, analysis, and applications. IEEE Transact. Pattern Anal. Mach. Intell., 22 (2000), 781–796.CrossRefGoogle Scholar
[14]Shechtman, E.; Irani, M.: Matching local self-similarities across images and videos, in IEEE Conf. Computer Vision and Pattern Recognition, USA, 2007.Google Scholar
[15]Van der Weken, D.; Nachtegael, M.; Kerre, E.E.: An overview of similarity measures for images, in IEEE Int. Conf. Acoustics, Speech, and Signal Processing, USA, 2002, 3317–3320.Google Scholar
[16]Pluim, J.P.W.; Maintz, J.B.A.; Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Transact. Med. Imaging, 22 (2003), 986–1004.Google Scholar
[17]Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J., 27 (1948), 379–423.Google Scholar
[18]Li, J.: Automatic Classification of Human Motions using Doppler Radar. Master thesis, School of Electrical, Computer, and Telecommunications Engineering, University of Wollongong, 2012.Google Scholar
[19]Molchanov, P.O.; Astola, J.T.; Egiazarian, K.O.; Totsky, A.V.: Target classification by using pattern features extracted from bispectrum-based radar Doppler signatures, in Int. Radar Symp., Germany, 2011.Google Scholar
[20]He, Y.; Savelyev, T.G.; Yarovoy, A.G.: Two-stage algorithm for extended target tracking by multistatic UWB radar. IEEE CIE Int. Conf. Radar, China, 2011.Google Scholar